DATA MANAGEMENT AND ANALYSIS CORE (DMAC): The DMAC will manage and store all relevant research data and provide data analytic support for all projects in COGEND. Our overall aims are to: 1) Maintain a database containing all instruments administered in COGEND, derived measures such as diagnoses, Fagerstrom scores and indices of nicotine consumption. 2) Provide well-documented, cleaned copies of all data sets to COGEND investigators on a regular basis and distribute data to investigators external to COGEND in accordance with agreed-upon procedures for sharing. Provide support and expertise in investigating the data contained in all data sets to COGEND and accepted non-COGEND investigators. 3) Maintain a web site for COGEND investigators. Access will require a sign-on and password. 4) Maintain data sets containing the genotypic data from the molecular genetics component of COGEND and data files describing the SNP markers. We will provide automated cleaning programs to check for genotyping errors, and provide an interface using a genome browser for annotation. 5) Maintain a dynamic process to provide oversight and support for the data analyses of the individual COGEND projects. The Data Analysis Committee will work with project investigators to provide formatted data for analysis, recommend analysis protocols to the individual projects, and supply expertise in necessary methodologic areas. These latter would include recommendations for dealing with multiple testing, prioritization and design of follow-up experiments, and advice on statistical techniques. 6) Transmit data to the NIDA Genetics Repository for sharing with the wider scientific community. The COGEND data represent a unique resource. The sample size is large enough to provide significant power to test the primary hypotheses. Moreover, one of the most important aims of COGEND is to make these resources available to the broader scientific community. This will be done in a way to permit alternative analyses for phenotypic classification, testing of new candidate genes, or testing integrative hypotheses across several domains.